Pre-stimulus phase and amplitude regulation of phase-locked responses is maximized in the critical state

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Abstract

Understanding why identical stimuli give differing neuronal responses and percepts is a central challenge in research on attention and consciousness. Ongoing oscillations reflect functional states that bias processing of incoming signals through amplitude and phase. It is not known, however, whether the effect of phase or amplitude on stimulus processing depends on the long-term global dynamics of the networks generating the oscillations. Here, we show, using a computational model, that the ability of networks to regulate stimulus response based on pre-stimulus activity requires near-critical dynamics—a dynamical state that emerges from networks with balanced excitation and inhibition, and that is characterized by scale-free fluctuations. We also find that networks exhibiting critical oscillations produce differing responses to the largest range of stimulus intensities. Thus, the brain may bring its dynamics close to the critical state whenever such network versatility is required.

Data availability

Source code required to run all simulations, as well as datasets and scripts required to generate all figures presented here, are available on figshare.

The following data sets were generated

(2020) Pre-stimulus phase and amplitude regulation of phase-locked responses is maximized in the critical state
figshare, 12167943.v1.

https://figshare.com/articles/Pre-stimulus_phase_and_amplitude_regulation_of_phase-locked_responses_is_maximized_in_the_critical_state/12167943

Article and author information

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Arthur-Ervin Avramiea

Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-0826-8269
Richard Hardstone

Perception and Brain Dynamics Laboratory, Departments of Neurology, Neuroscience and Physiology, and Radiology, Neuroscience Institute, New York University School of Medicine, New York, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-7502-9145
Jan-Matthis Lueckmann

Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany

Competing interests
The authors declare that no competing interests exist.
Jan Bim

Computer Science, Czech Technical University in Prague, Prague, Czech Republic

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2780-5610
Huib D Mansvelder

Department of Integrative Neurophysiology, Centre for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-1365-5340
Klaus Linkenkaer-Hansen

Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, Netherlands

For correspondence
k.linkenkaerhansen@vu.nl

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2140-9780

Funding

Netherlands Organization for Scientific Research (612.001.123)

Richard Hardstone
Klaus Linkenkaer-Hansen

Netherlands Organization for Scientific Research (406.15.256)

Arthur-Ervin Avramiea
Klaus Linkenkaer-Hansen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.